Modeling Portfolios that Contain Risky Assets
Optimization III: Conclusion
C. David Levermore
University of Maryland, College Park
Math 420: Mathematical Modeling
January 27, 2012 version
c 2011 Charles David Levermore
Modeling Portfolios that Contain Ris
Modeling Portfolios that Contain Risky Assets
Optimization II: Model-Based Portfolio
Management
C. David Levermore
University of Maryland, College Park
Math 420: Mathematical Modeling
January 26, 2012 version
c 2011 Charles David Levermore
Modeling Portfo
Modeling Portfolios that Contain Risky Assets
Optimization I: Model-Based Objective Functions
C. David Levermore
University of Maryland, College Park
Math 420: Mathematical Modeling
January 26, 2012 version
c 2011 Charles David Levermore
Modeling Portfoli
Modeling Portfolios that Contain Risky Assets
Stochastic Models I: One Risky Asset
C. David Levermore
University of Maryland, College Park
Math 420: Mathematical Modeling
January 26, 2012 version
c 2011 Charles David Levermore
Modeling Portfolios that Con
Modeling Portfolios that Contain Risky Assets
Portfolio Models III: Long Portfolios
with a Safe Investment
C. David Levermore
University of Maryland, College Park
Math 420: Mathematical Modeling
January 26, 2012 version
c 2011 Charles David Levermore
Mode
Modeling Portfolios that Contain Risky Assets
Portfolio Models II: Long Portfolios
C. David Levermore
University of Maryland, College Park
Math 420: Mathematical Modeling
March 25, 2012 version
c 2011 Charles David Levermore
Modeling Portfolios that Conta
Modeling Portfolios that Contain Risky Assets
Portfolio Models I: Portfolios with Risk-Free Assets
C. David Levermore
University of Maryland, College Park
Math 420: Mathematical Modeling
January 26, 2012 version
c 2011 Charles David Levermore
Modeling Por
Modeling Portfolios that Contain Risky Assets
Risk and Return III: Basic Markowitz
Portfolio Theory
C. David Levermore
University of Maryland, College Park
Math 420: Mathematical Modeling
January 26, 2012 version
c 2011 Charles David Levermore
Modeling Po
Modeling Portfolios that Contain Risky Assets
Risk and Return II: Markowitz Portfolios
C. David Levermore
University of Maryland, College Park
Math 420: Mathematical Modeling
January 26, 2012 version
c 2011 Charles David Levermore
Modeling Portfolios that
Modeling Portfolios that Contain Risky Assets
Risk and Return I: Introduction
C. David Levermore
University of Maryland, College Park
Math 420: Mathematical Modeling
January 26, 2012 version
c 2011 Charles David Levermore
Modeling Portfolios that Contain
Math 420, Spring 2012
Second Individual Homework
due Friday, 10 February, 2012
Exercise 1a. Compute mi , vij , and cij for each of the following groups of assets based on
daily, weekly, and monthly closing price data with uniform weights:
(i) Apple, Googl
Math 420, Spring 2012
First Individual Homework Problem
A dataset consisting of the national total numbers of births in the United
States on each day of 1978 can be found at
http:/www.math.umd.edu/evs/MATH420/Births1978.txt.
Using these data, do the follo
Fitting Linear Statistical Models to Data
by Least Squares III: Multivariate
Brian R. Hunt and C. David Levermore
University of Maryland, College Park
Math 420: Mathematical Modeling
January 25, 2012 version
Outline
1)
Introduction to Linear Statistical M
Fitting Linear Statistical Models to Data
by Least Squares II: Weighted
Brian R. Hunt and C. David Levermore
University of Maryland, College Park
Math 420: Mathematical Modeling
January 25, 2012 version
Outline of Three Lectures
1)
Introduction to Linear
Fitting Linear Statistical Models to Data
by Least Squares I: Introduction
Brian R. Hunt and C. David Levermore
University of Maryland, College Park
Math 420: Mathematical Modeling
January 25, 2012 version
Outline of Three Lectures
1)
Introduction to Line
New Model and
Nondimensionalization
Modied SIR Model
A standard extension to the SIR model adds terms
representing births and deaths that are proportional
to the overall population.
If were modeling an adult subpopulation that is either
infected or at r
Fitting Nonlinear Models to Data
SI Model
The SI model we discussed before is often written
dS /dt = pS I
d I /dt = pS I
where S is the susceptible population those at risk
to become infected at a given time and I is the
infectious population. For this m
Modeling Epidemics: Introduction
First Models
Preliminary goal: Model the spread of an infectious
(contagious) illness through a population.
Simplifying assumptions:
The total population N is constant in time.
A newly infected person becomes infectiou
Modeling Epidemics: Introduction
First Models
Preliminary goal: Model the spread of an infectious
disease through a population.
Simplifying assumptions:
The total population N is constant in time.
A newly infected person becomes infectious the
next da